A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis
In medical image analysis, major areas such as radiotherapy, surgery planning, and quantitative diagnostics benefit from shape modeling to facilitate solutions to analysis, segmentation and reconstruction problems. Heike Hufnagel proposes a mathematically sound statistical shape model using correspo...
Salvato in:
| Autore principale: | |
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| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Wiesbaden :
Vieweg+Teubner Verlag
[20..].
Cham : Springer Nature |
| Serie: | Aktuelle Forschung Medizintechnik Latest Research in Medical Engineering
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| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis, Texte imprimé, 9783834817228 |
| Riassunto: | In medical image analysis, major areas such as radiotherapy, surgery planning, and quantitative diagnostics benefit from shape modeling to facilitate solutions to analysis, segmentation and reconstruction problems. Heike Hufnagel proposes a mathematically sound statistical shape model using correspondence probabilities instead of 1-to-1 correspondences. The explicit probabilistic model is employed as shape prior in an implicit level set segmentation. Due to the particular attributes of the new model, the challenging integration of explicit and implicit representations can be done in an elegant mathematical formulation, thus combining the advantages of both explicit model and implicit segmentation. Evaluations are performed to depict the characteristics and strengths of the new model and segmentation method. The dissertation has received the Fokusfinder award 2011 by the Innovationsstiftung Schleswig-Holstein (ISH), the Basler AG and Philips Medical Systems. |
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| Descrizione del documento: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783834886002 |
| ISSN: | 2625-9354 |
| Accesso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |